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Smart Structures and Systems Volume 29, Number 6, June 2022 , pages 777-783 DOI: https://doi.org/10.12989/sss.2022.29.6.777 |
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Point-level deep learning approach for 3D acoustic source localization |
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Soo Young Lee, Jiho Chang and Seungchul Lee
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| Abstract | ||
| Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives. | ||
| Key Words | ||
| 3D acoustic source localization; 3D spherical beamforming; deep learning | ||
| Address | ||
| Soo Young Lee: Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk 37673, South Korea Jiho Chang: Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, South Korea Seungchul Lee: Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk 37673, South Korea; Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk 37673, South Korea | ||